code: def test_model_deploy_with_serverless_inference_config(
      sagemaker_session, tf_full_version, tf_full_py_version
  ):
      endpoint_name = unique_name_from_base(
          "sagemaker-tensorflow-serverless"
      )
      model_data = sagemaker_session.upload_data(
          path=os.path.join(
              tests.integ.DATA_DIR,
              "tensorflow-serving-test-model.tar.gz",
          ),
          key_prefix="tensorflow-serving/models",
      )
      with tests.integ.timeout.timeout_and_delete_endpoint_by_name(
          endpoint_name=endpoint_name,
          sagemaker_session=sagemaker_session,
          hours=2,
          sleep_between_cleanup_attempts=20,
          exponential_sleep=True,
      ):
          model = TensorFlowModel(
              model_data=model_data,
              role=ROLE,
              framework_version=tf_full_version,
              sagemaker_session=sagemaker_session,
          )
          predictor = model.deploy(
              serverless_inference_config=ServerlessInferenceConfig(),
              endpoint_name=endpoint_name,
          )
  
          input_data = {"instances": [1.0, 2.0, 5.0]}
          expected_result = {"predictions": [3.5, 4.0, 5.5]}
  
          result = predictor.predict(input_data)
          assert expected_result == result
  
imports: from __future__ import absolute_import
  import numpy
  import os
  import time
  import pytest
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.tensorflow import TensorFlow, TensorFlowProcessor, TensorFlowModel
  from sagemaker.utils import unique_name_from_base, sagemaker_timestamp
  import tests.integ
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, kms_utils, timeout
  from tests.integ.retry import retries
  from tests.integ.utils import gpu_list, retry_with_instance_list
  from tests.integ.s3_utils import assert_s3_file_patterns_exist
  from packaging.version import Version
label_desc: Use image node_id 10 for calling a global function with example usage: test_model_deploy_with_serverless_inference_config(sagemaker_session, tf_full_version, tf_full_py_version) without return types